Rain sensor with capacitive-inclusive circuit

ABSTRACT

A system and/or method for sensing the presence of moisture (e.g., rain) and/or other material(s) on a window such as a vehicle window (e.g., vehicle windshield, sunroof or backlite). In certain example embodiments of the invention, at least one sensing capacitor is supported by a window such as a vehicle windshield, the capacitor(s) having a field that is affected by moisture (e.g., rain) on a surface of the window. A sensing circuit outputs an analog signal that is based on and/or related to the capacitance(s) of the sensing capacitor(s). The analog output of the circuit may be converted to a digital signal, and subjected to processing (e.g., correlation) for determining whether moisture (e.g., rain, dew, fog, etc.) or the like is present on the surface of the window.

This application claims priority on U.S. Provisional Patent ApplicationNo. 60/757,479, filed Jan. 10, 2006, the disclosure of which is herebyincorporated herein by reference.

FIELD OF THE INVENTION

This invention relates to a system and/or method for sensing thepresence of rain and/or the disturbances or presence of other materialson a sheet(s) of glass such as a vehicle windshield, backlite, orsunroof. In certain example non-limiting embodiments, at least onesensing capacitor is supported by a window such as a vehicle windshield,the capacitor(s) having a field that is affected by moisture (e.g.,rain) on a surface of the window. A sensing circuit outputs an analogsignal that is based on and/or related to the capacitance(s) of thesensing capacitor(s). The analog output of the circuit may be convertedto a digital signal, and subjected to processing (e.g., correlation) fordetermining whether moisture (e.g., rain, dew, fog, etc.) or the like ispresent on the surface of the window.

BACKGROUND AND SUMMARY OF EXAMPLE EMBODIMENTS OF THE INVENTION

The presence of moisture (e.g., rain or condensation) and/or othermaterial or debris on vehicle windshields and/or backlites may createhazardous driving conditions for drivers, passengers, and pedestrians ifnot promptly removed. Wiper blades are a well-known, common way toremove such materials and reduce the hazards of driving during dangerousconditions. Rain sensors have been developed to detect the presence ofmoisture (e.g., rain or other condensation) on a vehicle windshield, andto turn on and off wipers, as necessary, when such moisture is detected.Automatically detecting rain, sleet, fog, and the like, and takingappropriate action—for example, turning on/off wiper blades at a properspeed—potentially reduces distractions to the driver, allowing thedriver to better concentrate on the road ahead. However, inappropriatelyturning on/off wipers or failing to actuate wipers when moisture ispresent may also create hazardous conditions. Moreover, such systems arealso susceptible to “dirt” distractions which may cause falsereads/wipes when dirt is on the windshield.

Certain conventional rain sensors are based on an electro-opticalconcept. According to certain such techniques, rain droplets are sensedsolely by measuring the change in the total internal reflection of alight beam off the glass-air interface. Other electro-optical techniqueshave attempted to analyze the brightness of a section of a window“image” to detect rain droplets or fog on a window. However, theseoptical techniques have limited sensing areas, are fairly expensive, andmay result in erroneous detection indications due to the use of opticalimaging as the sole detection method.

U.S. Pat. No. 6,144,022 to Tenenbaum et al. discloses an opticaltechnique for sensing rain on a vehicle windshield. This optical systemdivides a windshield into discrete rows and columns of pixels and thenoptically develops an “image” of the windshield. It creates a referenceimage of the windshield against which it compares future optical images.Unfortunately, the system of Tenenbaum suffers from certaindisadvantages. The Tenenbaum optical system is susceptible to erroneousdetections due to its reliance solely on optical imaging, and has alimited sensing area. The resolution of the optical image, and thus theoverall accuracy of the system, is dependent on the imaging optics. Thisnecessitates expensive optical components while requiringcomputationally intense data analysis, while the system is still subjectto the above disadvantages. Furthermore, Tenenbaum depends on theexistence of light to illuminate the water droplets through ambientmeans. Without naturally occurring ambient light (e.g., at night), thesystem will not properly function. LEDs may be used, but this tends tomake the system more complex and/or expensive, with additional potentialpoints of failure. Moreover, when using LEDs in the manner disclosed byTenenbaum, the system can be confused by sudden changes in ambientlight. For example, sudden changes in ambient light may occur when goingthrough a tunnel, coming around a corner and suddenly facing the sun,driving through a city with skyscrapers that block the sun, etc.,thereby leading to a potential for false readings/detections and falsewiper actuations.

U.S. Pat. No. 6,373,263 to Netzer teaches using capacitive rain sensorsand reading the differential current between two capacitors on thewindshield. Unfortunately, Netzer's system also has significantdisadvantages. For example, Netzer's system is sensitive only tochanges. Thus, for example, if there is already moisture (e.g., rain orcondensation) on a windshield because a vehicle was parked outsideduring a rain shower or fog, Netzer's system may not detect the samewhen the vehicle is started. Moreover, Netzer's system may be subject tocertain detrimental effects of electromagnetic interference (EMI),temperature changes, as well as interference from other sources. Forinstance, as external bodies (e.g., human hand, radio waves, etc.)interfere with the function of the capacitors, the charges of theexcitation and receiver electrodes may uncontrollably vary in Netzer,thereby leading to false alarms or detections. Thus, for example andwithout limitation, with Netzer's system, CB radios, microwaves,handheld devices, human contact with the windshield, groundable objects,and/or the like may undesirably interfere with the system, and thuspossibly produce false wipes and/or detections. Netzer's system is alsosubject to possible false reads caused by drastic temperature changes inview of the reference capacitor system utilized by Netzer, whereNetzer's reference capacitor has a different geometry/shape/size thanthe sensing capacitor.

Thus, it will be appreciated that there exists a need in the art for amoisture (e.g., rain) sensor that is efficient in operation and/ordetection. For example and without limitation, it may be desirable toprovide a rain sensor that overcomes one or more of the above-discusseddisadvantages. It is noted that all of the above-discussed disadvantagesneed not be overcome in certain example embodiments of this invention.

In certain example embodiments of this invention, there is provided arain sensor comprising: a sensing circuit comprising at least first andsecond sensing capacitors that are sensitive to moisture on an externalsurface of a window; the sensing circuit further comprising at least onemimicking capacitor that mimics at least charging and/or discharging ofat least one of the first and second sensing capacitors; wherein awriting pulse causes at least the first sensing capacitor to be charged,and an erasing pulse causes each of the first sensing capacitor and themimicking capacitor to substantially discharge; wherein presence of rainon the external surface of the window in a sensing field of the firstsensing capacitor causes a voltage at an output electrode of themimicking capacitor to fluctuate in a manner proportional to fluctuationof voltage at an output electrode of the first sensing capacitor, eventhough the rain is not present in a field of the mimicking capacitor;and wherein rain is detected based on an output signal from the outputelectrode of the mimicking capacitor, wherein the output signal is readat least between an end of the writing pulse and a beginning of theerase pulse.

In other example embodiments of this invention, there is provided amethod of detecting rain on a surface of a window, the methodcomprising: supplying first and second spaced apart writing pulses whichrespectively cause first and second sensing capacitors of a sensingcircuit to charge, wherein the first sensing capacitor charges when thesecond sensing capacitor is substantially discharged, and the secondsensing capacitor charges when the first sensing capacitor issubstantially discharged, so that the first and second sensingcapacitors are charged at different times; each of the first and secondsensing capacitors being sensitive to moisture on the surface of thewindow; supplying a first erasing pulse, between times of the first andsecond writing pulses, the first erasing pulse causing the first sensingcapacitor to substantially discharge, and supplying a second erasingpulse after the second writing pulse wherein the second erasing pulsecauses the second sensing capacitor to substantially discharge; whereina magnitude of an output of the sensing circuit is affected by presenceof rain on the surface of the window; and converting an analog outputsignal of the sensing circuit to a digital signal and based on thedigital signal determining whether rain is present on the surface of thewindow.

In certain example embodiments of this invention, there is provided arain sensor comprising: at least one sensing capacitor that is sensitiveto moisture on an external surface of a window, the sensing capacitorincluding a first capacitor electrode that receives a charging signaland a second capacitor electrode spaced apart from the first capacitorelectrode; and wherein the second capacitor electrode is floating sothat the sensing capacitor is isolated from ground. The floatingcharacteristic has been found to be advantageous in that it permitsfalse reads due to EMI or external objects (e.g., human hand) to bereduced or prevented.

In certain example embodiments of this invention, there is provided amethod of sensing the presence of moisture (e.g., rain, dew, fog, or thelike) on a vehicle window, the method comprising: receiving datarelating to at least two capacitors supported by the vehicle window;autocorrelating the data relating to each capacitor to obtainautocorrelated data; and determining, based at least on saidautocorrelated data, whether moisture is present on an exterior surfaceof the vehicle window. In certain example embodiments, the data relatingto the at least two capacitors is received from circuitry that receivesand/or reads capacitance data from the at least two capacitors. Incertain example embodiments, the data relating to the at least twocapacitors is output from circuitry that: (a) receives and/or reads dataand/or signals from the at least two capacitors, and/or (b) includes acapacitor(s) or other circuit element(s) that mimics or substantiallymimics charging and/or discharging of the at least two capacitors. Incertain example embodiments, the autocorrelation may be used as aninitial step to determine whether water may be present on the window.However, it is possible that the autocorrelation may also detect thepresence of other materials (e.g., dust or dirt) on the window becausethe correlation signatures of these materials can be different.

In certain example embodiments of this invention, there is provided amoisture sensor (e.g., rain sensor) for sensing the presence of moistureon a vehicle window, the moisture sensor comprising: one, two or morecapacitors; means for autocorrelating data from one, two, three, more,or all of the capacitors to obtain autocorrelated data; and means fordetermining, based at least on said autocorrelated data, whethermoisture is present on the vehicle window.

In certain example embodiments of this invention, cross-correlating datafrom the at least two capacitors may be performed so as to correlatedata from different capacitors to obtain cross-correlated data. Then,based at least on the cross-correlated data, a type and/or amount ofmoisture may be determined. The cross-correlated data may also orinstead be used to determine if the material detected via theautocorrelation is a material other than moisture such as dust or dirt,and if so then not actuating the wipers. In certain example embodiments,the cross-correlating may be performed after the autocorrelating whencertain conditions are met. As an example, the cross-correlation may beperformed so as to determine whether the moisture on the window is lightrain, heavy rain, fog, sleet, snow, or ice (a type of moisture).

In certain example embodiments of this invention, the autocorrelateddata from the capacitor(s) may be checked for negative values. When theautocorrelated data has negative value(s), then the system or method mayindicate that it is not raining and/or may not actuate windshieldwipers.

Moreover, in certain example embodiments, the system or method maycalculate whether a gradient of an autocorrelation curve associated withthe autocorrelated data is greater than one or some other predeterminedvalue; and if not then the system or method may indicate that it is notraining, park the wipers if they were moving, and/or not actuate wipersof the vehicle.

In certain example embodiments of this invention, the system or methodmay determine whether the shape of the autocorrelation curve associatedwith the autocorrelated data is different than a predeterminedautocorrelation curve associated with normalized non-disturbedautocorrelation data. When it is not different or substantiallydifferent, then it may be indicated that it is not raining, wipers maybe parked if they had been moving, and/or wipers may be not actuated.

In certain example embodiments of this invention, conditions checked forin the autocorrelation function include (i) the gradient of thenormalized autocorrelation function (e.g., when there is no disturbancethe absolute value of the gradient is unity and changes withdisturbance), (ii) the sign of the autocorrelation function (e.g., witha CB radio turned on or with a human hand on the windshield the valuesare oscillatory with positive and negative parts), and (iii) the shapeof the autocorrelation function as a function of time lag may also beused as a signature or footprint to distinguish rain from otherdisturbances, and this shape may also be used to distinguish betweendifferent nuances of rain or water content. Thus, in certain exampleinstances, cross-correlating of data from at least two capacitors isonly performed when one, two or all of the following conditions are met:(a) the autocorrelated data has no negative values; (b) a gradient of anautocorrelation curve associated with said autocorrelated data isgreater than one; and (c) the shape of the autocorrelation curveassociated with the autocorrelated data is different than apredetermined autocorrelation curve associated with normalizednon-disturbed autocorrelation data. Alternatively, (c) may be replacedwith (c′) the shape of the autocorrelation curve associated with theautocorrelated data matches or substantially matches a predeterminedautocorrelation curve associated with a known moisture pattern. Incertain example embodiments of this invention, a symmetry level of across-correlation curve associated with the cross-correlated data can bedetermined.

In certain example embodiments of this invention, it is possible tocompare the autocorrelation between various capacitors. In certainexample embodiments of this invention, such a comparison may be used totell the system whether to initiate a wipe if water is present on thewindow when the sensor system is turned on.

In certain example embodiments, a sensing capacitor array may include atleast n sensing capacitors, wherein n may be two, four, ten or any othersuitable number. The array may be any type of array such as a lineararray, any of the arrays shown in the figures, or any other type ofarray. Autocorrelating of data from and/or related to all or less thanall of the sensing capacitors may be performed to obtain theautocorrelated data.

In certain example embodiments of this invention, capacitors are formedbased on a fractal pattern. For example and without limitation, one ormore of the capacitors may be formed based on a fractal pattern, such asa Hilbert fractal pattern. Other capacitive fractal patterns may also beused, including but not limited to a Cantor set. These fractalstructures maximize or enlarge the periphery and thus result in a largecapacitance for a given area. The use of two dimensional fractal designsalso allows the sensor to occupy a small amount of physical space on thewindow while at the same time being electrically larger than itsphysical size. The concentration of lateral flux in a fractal geometrymay also allow the sensor to detect rain/water not necessarily spreadover the actual physical area of the sensor in certain exampleembodiments of this invention. Furthermore, in its higher iteration(s) afractal capacitor(s) has an attribute of being its own Faraday shield orquasi-Faraday shield. Also, in certain example embodiments, the rainsensor may be electrically connected to a Local Interconnect Bus of thevehicle.

In certain example embodiments of this invention, there is provided amethod of sensing the presence of moisture on a vehicle window such as awindshield, backlite or sunroof, the method comprising: receiving datafrom at least two capacitors supported by the vehicle window;correlating data from one or more of the capacitors to obtain correlateddata; determining, based at least on said correlated data, (a) whethermoisture is present on an exterior surface of the vehicle window, and/or(b) a type and/or amount of material present on an exterior surface ofthe vehicle window. For example and without limitation, the correlationmay be autocorrelation and/or cross-correlation.

In certain example embodiments of this invention, there is provided amethod of engaging vehicle windshield wiper(s) in response to detectedrain, the method comprising reading data from a capacitive array havingat least two capacitors; autocorrelating data from each capacitorindividually; determining from the autocorrelation data whether it israining; cross-correlating data from the capacitors; determining fromthe cross-correlated data a type and/or an amount of rain; engaging thewipers if rain is detected; and, stopping or not actuating the wipers ifone or both of the determining steps determines that it is not raining.In certain example embodiments, a symmetry level of thecross-correlation curve may be determined, and a wiper speed related tothe symmetry level may be selected. A wiper speed may be selected from aplurality of predetermined wiper speeds in certain example instances. Insome example embodiments, only a single wipe is initiated for boundaryconditions detected in one or both of the determining steps.

In certain example embodiments of this invention, there is provided amethod of engaging windshield wipers of a vehicle in response todetected rain, the method comprising reading data from a capacitivearray having at least two capacitors; mathematically comparing data fromeach capacitor individually (e.g., autocorrelating); determining fromthe mathematically compared individual capacitor data whether it israining; mathematically comparing data from different capacitors (e.g.,cross-correlating); determining from the mathematically compareddifferent capacitor data a type and/or an amount of rain; engaging thewipers if rain is detected; and, stopping or not actuating the wipers ifone or both of the determining steps determines that it is not raining.

In certain example embodiments, a sigma-delta modulator or othersuitable circuit or software may be used to perform an analog-to-digital(A/D) conversion of data from the capacitive array. Additionally, incertain example embodiments, a software or other type of comparator mayperform at least one of checking autocorrelation data for negativevalues, calculating whether a gradient of autocorrelation data isgreater than one, and/or attempting to match or substantially match ashape of autocorrelation data with autocorrelation data stored in adatabase. In certain instances, the correlating engine computescross-correlations when all conditions tested for by the comparator aremet.

In certain example embodiments of this invention, there is provided asystem or method for engaging windshield wipers in response to detectedrain, the system (or method) comprising a capacitive array having atleast two capacitors; circuitry that reads capacitance data from thecapacitive array; a correlating engine or correlator that autocorrelatesdata from the circuitry to determine the existence of rain, andcross-correlates data from the circuitry to determine a type and/or anamount of rain if it is determined that rain exists; and, a wiper motorthat is capable of receiving a signal for directing whether the wipersshould move or stop. In certain example embodiments, a symmetry level ofa cross-correlation curve is computed, and the wiper motor may select awiper speed related to the symmetry level.

In certain example embodiments, a rain sensor comprises at least twosensing devices (e.g., sensing capacitors or the like) that are affectedby rain on a surface of a window; circuitry that provides an outputrelated to the sensing devices; and at least one correlating engine that(a) autocorrelates information from said circuitry to determine whetherrain is present, and/or (b) cross-correlates information from saidcircuitry to determine how fast to operate at least one wiper of avehicle and/or an amount of rain.

In certain example embodiments, a method or system for engaging windowwiper(s) in response to detected rain is provided and comprises acapacitive array having at least two capacitors; circuitry that readscapacitance data from the capacitive array; an algorithm thatmathematically determines existence of rain on the window based on datafrom the circuitry, and mathematically quantifies a type and/or amountof rain if it is determined that rain exists; and, a wiper motor capableof receiving a signal(s) directing whether the wiper(s) should move orstop.

BRIEF DESCRIPTION OF THE DRAWINGS

These and other features and advantages will be better and morecompletely understood by reference to the following detailed descriptionof exemplary illustrative embodiments in conjunction with the drawings,of which:

FIG. 1(a) is a block diagram of components of an exemplary rain sensoraccording to an example embodiment of this invention.

FIG. 1(b) is a cross sectional view of a rain sensor according to anexample embodiment of this invention, that may use the features of FIG.1(a) and/or one or more of FIGS. 2-12.

FIG. 1(c) is a cross sectional view of a rain sensor according toanother example embodiment of this invention, that may use the featuresof FIG. 1(a) and/or one or more of FIGS. 2-12.

FIG. 1(d) is a cross sectional view of a rain sensor according toanother example embodiment of this invention, that may use the featuresof FIG. 1(a) and/or one or more of FIGS. 2-12.

FIG. 1(e) is a cross sectional view of a rain sensor according toanother example embodiment of this invention, that may use the featuresof FIG. 1(a) and/or one or more of FIGS. 2-12.

FIG. 1(f) is a cross sectional view of a rain sensor according toanother example embodiment of this invention, that may use the featuresof FIG. 1(a) and/or one or more of FIGS. 2-12.

FIG. 2A is an exemplary optimized pattern for a quadrant capacitivearray based on Hilbert fractals, where such capacitors may be providedon the window as a sensor array in the embodiments of one or more ofFIGS. 1(a)-1(f) and 4-12 for example.

FIG. 2B is another exemplary optimized pattern for a quadrant capacitivearray, where such capacitors may be provided on the window as a sensorarray in the embodiments of one or more of FIGS. 1(a)-1(f) and 4-12 forexample.

FIG. 3 is an enlarged picture of yet another exemplary quadrantcapacitive array, where such capacitors may be provided on the window asa sensor array in the embodiments of one or more of FIGS. 1(a)-1(f) and4-12 for example.

FIG. 4 is an example circuit diagram including exemplary circuitry usedfor a write clock pulse in readout electronics, for use in one or moreof the embodiments of FIGS. 1(a)-1(f) and 5-12 for example.

FIG. 5 is an example circuit diagram including exemplary circuitry usedfor an erase clock pulse in readout electronics, for use in one or moreof the embodiments of FIGS. 1(a)-1(f), 4 and 6-12 for example.

FIG. 6 is an exemplary timing diagram derived from readout circuitry ofFIGS. 4-5.

FIG. 7 is an exemplary flowchart or state diagram showing howautocorrelation and cross-correlation data may be used to control wipersaccording to an example embodiment of this invention, which may be usedin conjunction with one of more of FIGS. 1-6 and 8-12.

FIG. 8 is an exemplary flowchart showing how autocorrelation andcross-correlation data can be used to control wipers according to anexample embodiment of this invention, which may be used in conjunctionwith one of more of FIGS. 1-7 and 9-12.

FIG. 9 is an exemplary stylized view of how a rain droplet might travelacross a windshield.

FIG. 10 is an graph plotting example experimentally-obtained maximumvalues of non-normalized autocorrelations for different disturbances.

FIG. 11A is an example experimentally-obtained autocorrelation snapshotindicative of heavy rain.

FIG. 11B is an example experimentally-obtained autocorrelation snapshotindicative of a light mist.

FIG. 11C is an example experimentally-obtained autocorrelation snapshotindicative of CB radio interference.

FIG. 11D is an example experimentally-obtained autocorrelation snapshotindicative of a grounded body with a voltage.

FIG. 12A is an exemplary correlation matrix indicative of light rain.

FIG. 12B is an exemplary correlation matrix indicative of heavy rain.

FIG. 13 is an example of autocorrelation according to an exampleembodiment of this invention.

FIG. 14 is a chart setting forth example cross-correlation data fromcapacitors C1, C2 according to examples of certain embodiments of thisinvention.

FIG. 15 is a crosscorrelation graph, plotting crosscorrelation valuesversus time lags (the time lags are in terms of microseconds in the timedomain) according to an example of this invention, using certain signalsfrom FIG. 14.

FIG. 16 is a crosscorrelation graph, plotting crosscorrelation valuesversus time lags (the time lags are in terms of microseconds in the timedomain) according to an example of this invention, using certain signalsfrom FIG. 14.

FIG. 17 is a crosscorrelation graph, plotting crosscorrelation valuesversus time lags (the time lags are in terms of microseconds in the timedomain) according to an example of this invention, using certain signalsfrom FIG. 14.

FIG. 18 is a crosscorrelation graph, plotting crosscorrelation valuesversus time lags (the time lags are in terms of microseconds in the timedomain) according to an example of this invention, using certain signalsfrom FIG. 14.

FIG. 19 is a crosscorrelation graph, plotting crosscorrelation valuesversus time lags (the time lags are in terms of microseconds in the timedomain) according to an example of this invention, using certain signalsfrom FIG. 14.

FIG. 20 is a crosscorrelation graph, plotting crosscorrelation valuesversus time lags (the time lags are in terms of microseconds in the timedomain) according to an example of this invention, using certain signalsfrom FIG. 14.

FIG. 21 is a crosscorrelation graph, plotting crosscorrelation valuesversus time lags (the time lags are in terms of microseconds in the timedomain) according to an example of this invention, using certain signalsfrom FIG. 14.

FIG. 22 is a crosscorrelation graph, plotting crosscorrelation valuesversus time lags (the time lags are in terms of microseconds in the timedomain) according to an example of this invention, using certain signalsfrom FIG. 14.

FIG. 23 is a crosscorrelation graph, plotting crosscorrelation valuesversus time lags (the time lags are in terms of microseconds in the timedomain) according to an example of this invention, using certain signalsfrom FIG. 14.

FIG. 24 is a crosscorrelation graph, plotting crosscorrelation valuesversus time lags (the time lags are in terms of microseconds in the timedomain) according to an example of this invention, using certain signalsfrom FIG. 14.

FIG. 25 is a block diagram illustrating circuitry and/or processing ofsignals according to an example embodiment of this invention where asensing capacitor (e.g., C1) is present, including sigma-deltamodulation.

FIG. 26 is a block diagram illustrating circuitry and/or processing ofsignals according to an example embodiment of this invention where aplurality of capacitors (e.g., C1-C4) are present, including sigma-deltamodulation.

FIG. 27 is a block diagram illustrating sigma-delta modulation accordingto an example embodiment of this invention; this processing beingperformed in circuitry, firmware and/or software.

FIGS. 28(a) and 28(b) are schematic diagrams illustrating advantages ofusing floating electrodes for sensing capacitors (e.g., C1-C4) accordingto certain example embodiments of this invention.

DETAILED DESCRIPTION OF EXAMPLE EMBODIMENTS OF THE INVENTION

Referring now more particularly to the accompanying drawings in whichlike reference numerals indicate like parts throughout the severalviews.

In certain example embodiments of this invention, a moisture (e.g.,rain) sensor system and/or method is provided and includescapacitance-based detection which translates a physical input signal(e.g., the presence of a drop of water on a windshield, or the like)into a digital electrical voltage signal which is received andinterpreted by a software program(s) or circuit(s) that decides whetherwindshield wipers should be activated, and, if so, optionally theirproper speed. Thus, capacitive coupling is used to detect water and/orother material in the exterior surface of a window such as a vehiclewindshield, sunroof, and/or backlite. It will be appreciated thatcomputational methods may be performed by hardware or a combination ofhardware and software in different example embodiments of thisinvention. In certain example embodiments of this invention, noreference capacitance or capacitor is needed (i.e., no compensationcapacitor is needed).

Certain example embodiments of this invention take advantage of apermittivity equation, which gives a physical quantity that describeshow an electric field affects and is affected by a medium. An examplebasic permittivity equation is:D=ε ₀ E+P,where D is electrical flux, ε₀ is the dielectric constant of a vacuum, Eis an electrical field (e.g., the voltage setup between plates orelectrodes divided by distance, or V/m), and P is polarization.Polarization P can be further described mathematically as:P=ε _(r)ε₀ E,where ε_(r) is relative permittivity (e.g., the dielectric constant ofwater, ice, dirt or anything else that could be on an exterior surfaceof a window such as a windshield). In general, a high value of ε_(i)will correspond to high polarizability. The permittivity of glass isapproximately 8, and the permittivity of water is approximately 85. Bysubstitution and factorization, then, the permittivity equation can berewritten as:D=ε ₀(ε_(r)+1)E.In this form, it will be appreciated that D is the response toexcitation E.

Capacitance C is given by C=Q/V, where Q is the charge and V is thepotential, in volts. Additionally, C=Φ/V, where Φ is the electric fluxassociated with charge Q. By Gauss' Law:Φ=o∫ _(s) E·dA,where dA is the area of a differential square on the closed surface S.By substitution, then, it becomes clear that capacitance is related topotential difference:C=∫DdA/V.

These equations form the basis of an example technique for measuring theinteraction of water on glass by using a sensor with a capacitive arrayto probe above the window (e.g., glass). In particular, data from asensor including at least one, or two or more, capacitor(s) (e.g., C1,C2, C3, etc.) may be used to detect whether moisture (e.g., rain, or thelike) is present on an exterior surface of a window such as a vehiclewindshield or backlite. The above equations illustrate that the presenceof water on the surface of a window can affect the capacitance of anappropriately positioned sensing capacitor.

FIG. 1(a) is a block diagram of example components of a moisture (e.g.,rain) sensor according to an example embodiment of this invention. Powersupply 10 is connected to readout electronics 12 which may include oneor more of hardware, firmware, and/or software. As will be described ingreater detail below, the sensor includes one or more capacitors so asto make up a capacitive sensor 5 in certain example embodiments. Whiledifferent types of capacitors may be used, capacitors each having a pairof approximately coplanar electrodes arranged in a fractal pattern maybe used in the sensor in certain example embodiments of this invention.In certain example embodiments, a fractal pattern may be divided into acapacitive array. Data from and/or related to the sensing capacitor(s)of the capacitive sensor 5 is received and read by readout electronics12 which may be made up of one or more of hardware, firmware and/orsoftware. Readout electronics 12 pick up electrical noise and convertthe same to digital signal(s). This digital signal(s) is passed tocomputing module 14 (which may be made up of one or more of hardware,firmware and/or software) which determines what action the wipers shouldtake. For example, the wipers might initiate a single wipe, low-speedwipes, high-speed wipes, etc., based on the data analyzed from and/orrelated to the capacitive sensor. The wipers also may be caused to turnoff, slow/increase the speed at which they are wiping, etc., based onthe data analyzed from and/or related to the capacitive sensor. Wipercontrol system motor 16 receives instructions from computing module 14and directs wipers 18 to take the appropriate action.

In certain example embodiments, the capacitive sensor 5 interfaces witha Local Interconnect Bus (LIN bus) of a vehicle. A LIN bus (not shown)typically is a serial bus to which slave devices in an automobile areconnected. A LIN bus typically executes a handshake(s) with slavedevices to ensure that they are, for example, connected and functional.Additionally, a LIN bus may provide other information to slave devices,such as, for example, the current time.

In certain example embodiments of this invention, the capacitive sensor5 includes a plurality of capacitors in the form of any suitable array.

FIG. 1(b) is a cross-sectional view of a vehicle window including amoisture sensor according to an example embodiment of this invention. Awindshield of the vehicle includes inner glass substrate 1 and outerglass substrate 2 that are laminated together via a polymer-inclusiveinterlayer 3 of a material such as polyvinyl butyral (PVB) or the like.An optional low-e (low emissivity) coating 4 may be provided on theinner surface of the exterior glass substrate 2 (or even on the surfaceof substrate 1) in certain example embodiments of this invention. Alow-E coating 4 typically includes at least one thin IR reflecting layerof a material such as silver, gold or the like sandwiched between atleast first and second dielectric layers of material such as siliconnitride, tin oxide, zinc oxide, or the like. Example low-E coatings 4,for purposes of example and without limitation, are described in U.S.Pat. Nos. 6,686,050, 6,723,211, 6,782,718, 6,749,941, 6,730,352,6,802,943, 4,782,216, 3,682,528, and 6,936,347, the disclosures of whichare hereby incorporated herein by reference.

FIG. 1(b) illustrates an example capacitor of the capacitive sensor.While the capacitive sensor of FIG. 1(a) typically includes a pluralityof capacitors in an array, only one capacitor of the sensor is shown inFIG. 1(b) for purposes of simplicity. The other capacitors are similarin cross section to the one shown in FIG. 1(b) in certain exampleembodiments of this invention. The example capacitor (C1, C2, C3 or C4)of the capacitive sensor shown in FIG. 1(b) includes a pair of spacedapart coplanar or substantially coplanar capacitor electrodes 7 and 8.The electrodes 7 and 8 are of a conductive material that may be printedor otherwise formed on the window. For example, the capacitor electrodes7 and 8 of the sensing capacitor may be made of or include silver, ITO(indium tin oxide), or other suitable conductive material. In certainexample embodiments, the capacitor shown in FIG. 1(b) is affected by arain droplet on the exterior surface of the window because electricfield Es of the capacitor extends to or beyond the exterior surface ofthe window as shown in FIG. 1(b) and thus can interact with the raindroplet or other material on the window's exterior surface. Signalsreceived from and/or relating to the sensing capacitor(s) and analysisthereof is described herein.

In the FIG. 1(b) embodiment, an opaque insulating layer (e.g., blackfrit or enamel, or the like) 9 is provided on the window over theelectrodes 7 and 8 in order to shield the electrodes 7, 8 from the viewof a passenger(s) sitting inside the vehicle. Thus, it will beappreciated that the opaque layer 9 is only provided on a small portionof the window, including in the area where the capacitive array of therain sensor's array of capacitors is located. In certain exampleinstances, the rain sensor's capacitive array and thus the opaque layer9 may be located on a vehicle windshield in an area proximate therear-view mirror mounting bracket. In certain example embodiments, theopaque layer 9 (e.g., black frit or enamel) may contact the fractalpattern of the capacitor electrodes 7, 8 directly because the layer 9 isnot conductive. However, even if a black frit layer 9 were conductive(which is possible), its dielectric constant is close to that of waterso that it will not adversely interfere with the capturing of data fromand/or related to the capacitors C1-C4 and associated analysis.

FIG. 2A is a top or plan view illustrating an example capacitive sensorarray including four capacitors C1, C2, C3 and C4. Each of thesecapacitors C1, C2, C3 and C4 includes first and second spaced apartcoplanar capacitor electrodes 7 and 8 as shown in FIG. 1(b) (or any ofFIGS. 1(c)-1(f)). The capacitor electrodes 7 and 8 of each capacitorC1-C4 may be made of conductive silver frit or the like as shown in FIG.2A. Moreover, in certain example embodiments, there may be a gap 22 offrom about 0.2 to 1.5 mm, more preferably from about 0.3 to 1.0 mm(e.g., 0.6 mm), between the coplanar capacitor electrodes 7 and 8 of acapacitor (C1, C2, C3 and/or C4) as shown in FIG. 2A. In the FIG. 2Aembodiment, the capacitors C1-C4 are covered with an insulating blackfrit layer 9 which is the same as the opaque layer 9 discussed abovewith respect to FIG. 1(b). In FIG. 2A, a contact pad array is providedin the center of the sensor array, and includes four contact padselectrically connected to the respective electrodes 7 of the capacitorsC1-C4, and four contact pads electrically connected to the respectiveelectrodes 8 of the capacitors C1-C4. An example contact pad is referredto by reference numeral 28 in FIG. 2A. The four white colored contactpads 28 in FIG. 2A are electrically connected to the respectivecapacitor electrodes 7 of capacitors C1-C4, whereas the dark greycolored contact pads 28 in FIG. 2A are electrically connected to therespective capacitor electrodes 8 of the capacitors C1-C4. All of thesensing capacitors C1-C4 are sensitive to moisture such as rain on theexternal surface of the window.

In the FIG. 2A embodiment, each of the capacitors C1-C4 of thecapacitive sensor is formed using fractal geometry. In particular, eachof the coplanar electrodes 7 and 8 of each capacitor C1-C4 is formedwith a fractal geometry. Fractal design patterns allow, for example, ahigh capacitance to be realized in a small area, and are thereforedesirable over other geometries in certain example rain sensorapplications. Fractal geometry may be grouped into (a) random fractals,which may be called chaotic or Brownian fractals and include a randomnoise component, and (b) deterministic or exact fractals. Indeterministic fractal geometry, a self-similar structure results fromthe repetition of a design or motif (or “generator”) (i.e.,self-similarity and structure at all scales). In deterministic or exactself-similarity, fractal capacitors may be constructed through recursiveor iterative means. In other words, fractals are often composed of orinclude many copies of themselves at different scales.

In the FIG. 2A embodiment, it can be seen that the coplanar electrodes 7and 8 of each capacitor (where the electrodes 7 and 8 are shown but notlabeled in FIG. 2A due to the dark color of the frit 9, but are spacedapart by gaps 22) have fractal geometries and are arranged substantiallyparallel to each other throughout the meandering length of eachcapacitor. In other words, each electrode 7, 8 of a given capacitor(e.g., C1, C2, C3 or C4) has a meandering shape in the fractal geometry,but stays substantially parallel to the other electrode (the other of 7,8) of the capacitor throughout the meandering length of the capacitor.The overall length of each capacitor (e.g., C1), along the meanderinglength of the fractal, is from about 25 to 200 mm in certain exampleembodiments of this invention, more preferably from about 30 to 90 mm,with an example being about 50 mm.

The fractal pattern of FIG. 2A is a Hilbert fractal pattern. Theelectrodes 7, 8 of the capacitors C1-C4 in the FIG. 2A embodiment form aHilbert fractal pattern, for purposes of example only and withoutlimitation. In particular, the capacitors shown in FIG. 2A are shaped ina third-order Hilbert fractal manner. Hilbert fractals are continuousspace-filling fractals, with fractal dimensions of two. This means thathigher-order fractals will become more square-like. A Hilbert fractalcan be formed by using the following L-system:Hilbert{Angle 90Axiom XX=−YF+XFX+FY−Y=+XF−YFY−FX+}where “Angle 90” sets the following rotations to 90 degrees, X and Y aredefined functions, “F” means “draw forward”, “+” means “turncounterclockwise”, and “−” means “turn clockwise”. While Hilbert fractalgeometries may be used in forming the capacitors C1-C4 in certainexample embodiments of this invention, this invention is not so limited,and other types of fractals may also be used to form the capacitorshapes. For example, the capacitor electrodes 7, 8 of capacitors C1-C4in any embodiment herein may be formed using any of the fractal designsdisclosed in any of U.S. Pat. Nos. 6,552,690, 6,104,349, 6,140,975,6,127,977, 6,084,285, 6,975,277, the disclosures of which are herebyincorporated herein by reference. In certain example embodiments of thisinvention, as shown in FIGS. 2A, 2B and 3, all sensing capacitors of thesensing array may be identical or substantially identical in shape.

In preferred embodiments, each of the capacitors C1-C4 in the sensorarray may be electrically floating (this may be called a virtual groundin certain example instances) so as to not have a fixed common groundsuch as a fixed zero volts, and/or spatially separated or the like whichmay be useful with respect to the correlation functions. Additionally,the lack of a common ground means that the capacitive array will not besubject to adverse effects from interference such as, for example, EMIinterference thereby reducing the potential for false wipes, falsedetections, and the like.

The fractal design for capacitors C1-C4 may be used in any of theembodiments of FIGS. 1(a)-1(f).

FIG. 1(c) is a cross sectional view of another example embodiment ofthis invention, which may use the system of FIGS. 1(a) and one or moreof the embodiments of FIGS. 2-12. In the FIG. 1(c) embodiment, thevehicle window (e.g., backlite) is made up of only one glass sheet 10,and the electrodes 7, 8 of the capacitor are provided on, directly orindirectly, the interior major surface of the glass sheet 10. Thecapacitor (e.g., C1) shown in FIG. 1(c) is designed such that it isaffected by a rain droplet (or other material) on the exterior surfaceof the window because the electric field Es of the capacitor extends toor beyond the exterior surface of the window as shown in FIG. 1(c) andthus can interact with the rain droplet or other material on thewindow's exterior surface. Each of the capacitors C1-C4 is formed in asimilar manner. It is noted that the use of the word “on” herein coversboth directly on and indirectly on, and is not limited to physicalcontact or touching unless expressly stated. An opaque layer 9, similarto that shown in the FIG. 1(b) embodiment, may also be provide in theFIG. 1(c) embodiment if desired.

FIG. 1(d) is a cross sectional view of another example embodiment ofthis invention, which may use the system of FIGS. 1(a) and one or moreof the embodiments of FIGS. 2-12. In the FIG. 1(d) embodiment, thevehicle window (e.g., laminated windshield) includes glass sheets 1 and2 laminated together via polymer based interlayer 3, and optionallyincludes a low-E coating 4 on either substrate 1 or substrate 2. TheFIG. 1(d) embodiment differs from the FIG. 1(b) embodiment in that theelectrodes 7, 8 of the capacitor are provided on the major surface ofglass substrate 1 that is furthest from the vehicle interior. Thecapacitor electrodes 7, 8 may contact the polymer interlayer 3 in thisembodiment, in certain example instances. The capacitor (e.g., C1, C2,C3 or C4) shown in FIG. 1(d) is designed such that it is affected by arain droplet (or other material) on the exterior surface of the windowbecause the electric field Es of the capacitor extends to or beyond theexterior surface of the window as shown in FIG. 1(d) and thus caninteract with the rain droplet or other material on the window'sexterior surface. Each of the capacitors C1-C4 of the sensor array isformed in a manner similar to that shown for the capacitor of FIG. 1(d).Opaque layer 9 may also be provided in the FIG. 1(d) embodiment ifdesired, over a portion of the window so as to shield the capacitorelectrodes from a vehicle passenger's view. In the embodiment shown inFIG. 1(d), the electrodes 7 and 8 may be formed of a conductive silverfrit or ITO printed or patterned directly on and contacting the surfaceof substrate 1. However, this invention is not so limited, and theelectrodes 7 and 8 of one or more capacitors of the sensor may insteadbe formed and patterned from a metallic conductive IR reflecting layer(e.g., silver based layer) of a low-E coating 4 that is supported by thewindow.

FIG. 1(c) is a cross sectional view of another example embodiment ofthis invention, which may use the system of FIGS. 1(a) and one or moreof the embodiments of FIGS. 2-12. In the FIG. 1(e) embodiment, thevehicle window (e.g., laminated windshield) includes glass sheets 1 and2 laminated together via polymer based interlayer 3, and optionallyincludes a low-E coating 4 on either substrate 1 or substrate 2. TheFIG. 1(e) embodiment differs from the FIG. 1(b) embodiment in that theelectrodes 7, 8 of the capacitor (e.g., C1, C2, C3 or C4) are providedon the major surface of the exterior glass substrate 2 that is closestto the vehicle interior. The capacitor electrodes 7, 8 may contact thepolymer interlayer 3 in this embodiment, in certain example instances.The capacitor (e.g., C1, C2, C3 or C4) shown in FIG. 1(e) is designedsuch that it is affected by a rain droplet (or other material) on theexterior surface of the window because the electric field Es of thecapacitor extends to or beyond the exterior surface of the window asshown in FIG. 1(e) and thus can interact with the rain droplet or othermaterial on the window's exterior surface. Each of the capacitors C1-C4of the sensor array is formed in a manner similar to that shown for thecapacitor of FIG. 1(e). Opaque layer 9 may also be provided in the FIG.1(e) embodiment if desired, over a portion of the window so as to shieldthe capacitor electrodes from the view of a vehicle passengers(s).

FIG. 1(f) is a cross sectional view of another example embodiment ofthis invention, which may use the system of FIGS. 1(a) and one or moreof the embodiments of FIGS. 2-12. In the FIG. 1(f) embodiment, thevehicle window (e.g., laminated windshield) includes glass sheets 1 and2 laminated together via polymer based interlayer 3, and optionallyincludes a low-E coating 4 on either substrate 1 or substrate 2. TheFIG. 1(f) embodiment differs from the FIG. 1(b) embodiment in that theelectrodes 7, 8 of the capacitor (e.g., C1, C2, C3 or C4) are providedon the major surface of the interior glass substrate 1 that is closestto the vehicle interior, via support member 12. The support member 12,located between the glass substrate 1 and the electrodes 7, 8, may bemade of glass, silicon or the like. The capacitor (e.g., C1, C2, C3 orC4) shown in FIG. 1(e) is designed such that it is affected by a raindroplet (or other material) on the exterior surface of the windowbecause the electric field Es of the capacitor extends to or beyond theexterior surface of the window as shown in FIG. 1(f) and thus caninteract with the rain droplet or other material on the window'sexterior surface. Each of the capacitors C1-C4 of the sensor array isformed in a manner similar to that shown for the capacitor of FIG. 1(f).Opaque layer 9 may also be provide in the FIG. 1(f) embodiment ifdesired, over a portion of the window so as to shield the capacitorelectrodes 7, 8 from the view of a vehicle passengers(s).

FIG. 2B is a plan view of an example pattern for a quadrant capacitivearray of fractal shaped capacitors C1-C4 for the capacitive sensoraccording to another example embodiment of this invention. The fourcapacitors shown in FIG. 2B are similar to those of FIG. 2A, except forthe precise shapes thereof. The FIG. 2B capacitors may be used in any ofthe embodiments of FIGS. 1(a)-(f). The super-imposed dashed lines showthe divisions into four distinct capacitors C1-C4. The outer line widthmay be about 2 mm, and the inner line width about 1 mm, in certainexample embodiments.

FIG. 3 is an enlarged picture of another exemplary quadrant capacitivearray of fractal shaped capacitors C1-C4 for the capacitive sensoraccording to another example embodiment of this invention. The fourcapacitors shown in FIG. 3 are similar to those of FIGS. 2A and 2B,except for the precise shapes thereof. The FIG. 3 fractal capacitors maybe used in any of the embodiments of FIGS. 1(a)-(f). The superimposedlines show example division between capacitors C1-C4 in FIG. 3. It willbe appreciated that some example embodiments may have capacitive arrayswith as few as two capacitors. However, it is preferable to have atleast four capacitors in certain example embodiments to pick up andderive nuances in perturbations.

The use of the fractal geometry for the sensing capacitors C1-C4 can beadvantageous in reducing false readings due to EMI interference incertain example embodiments of this invention. In particular, fractalsat high iterations help reduce EMI interference issues, because theFaraday cage or quasi-Faraday cage of the fractal at high iterationsreduces EMI coupling thereby reducing adverse effects of EMIinterference. Fractals at high iterations form quasi-Faraday cages.

In certain example embodiments of this invention, the readoutelectronics look at the interaction of rain and/or other perturbationson the window. In certain example embodiments, this process may beaccomplished by sequentially charging capacitors, reading their data,quantizing that data, and/or erasing the charges.

FIG. 4 is a circuit diagram of a sensing or read-out circuit accordingto an example embodiment of this invention. The sensing circuit of FIG.4 may be made up of the electronics unit 12 and the capacitive sensorarray 5 of FIG. 1. Any of the capacitors of FIGS. 1(b)-1(f), 2A, 2B,and/or 3 may be used as the capacitors C1-C4 of the circuit in FIG. 4.The FIG. 4 circuitry is used for a write clock pulse in readoutelectronics, in certain example embodiments of this invention.Transistors Q1, Q2, and Q7 are p-channel MOSFETs, with transistors Q1and Q2 primarily being responsible for a write phase. Transistors Q5 andQ6 are n-channel MOSFETs.

Still referring to FIG. 4, during a write phase a write pulse Clk_(Wr)is input to the gate of transistor Q7, which functions like a resistoror switch, charging one or more of the capacitors C1-C4 of the sensorcapacitance C_(s). FIG. 6 includes certain signals used in the FIG. 4circuit in the write cycle. In the write cycle, Transistor Q1 is in asaturated mode, since its gate and drain are connected, so that Q1 ison. Q4, Q5 and Q6 are turned off, and Q2 is on during the write mode.Transistors Q3 and Q4 are optional. When Q7 is turned on by the writepulse, we have a write cycle, and Vcc appears at Cs via A and chargesone or more of the capacitors C1-C4 of the sensor capacitance Cs. V_(cc)may be a constant voltage, such as 5V, in certain example embodiments.One or more of the capacitors C1-C4 may be charged at a time during awrite cycle. However, in certain example embodiments of this invention,the circuit charges and reads the capacitors C1, C2, C3 and C4, one at atime (e.g., see FIGS. 6). Thus, during one write cycle, only one of thecapacitors C1, C2, C3 or C4 is charged in certain example embodiments ofthis invention.

The above process described for the left side of the FIG. 4 sensingcircuit is essentially mirrored on the opposite or right side of theFIG. 4 circuit. As current flows through the left-side branch, currentalso flows at B through the right-side branch, and changes to C_(s) aremimicked, or substantially mimicked in internal mimicking capacitanceC_(int). When Q7 is turned on, current also flows through Q2 (which ison) and charges C_(int) using Vcc. Thus, the charging of one of thecapacitors C1-C4 is mimicked by the charging of capacitor C_(int). Inother words, C_(int) is charged to the same degree, or substantially thesame degree, as the capacitor (e.g., C1) being charged on the other sideof the FIG. 4 circuit. The output voltage of the FIG. 4 circuit, Vout(or Vo), is based on C_(int) and is taken at or proximate an electrodeof the capacitor C_(int) as shown in FIG. 4. An example formulareflecting Vout (or Vo) is shown at the bottom of FIG. 4. Accordingly,it will be appreciated that the output Vout (or Vo) of the FIG. 4-5circuit is related to and based on the capacitors C1-C4 of the sensorCs. More specifically, the output Vout of the FIG. 4-5 circuit isrelated to and indicative of the status of capacitors C1-C4 and theeffects on those capacitors of moisture on the exterior window surface,even though Vout is not taken directly from capacitors C1-C4. Inparticular, Vout (or Vo) is read out during the write cycle, due to thewrite pulse shown in FIG. 4 (see also FIG. 6). In the formula at thebottom of FIG. 4 for Vout, W1 is for Q1, W2 is for Q2, L1 is for Q1, L2is for Q2, where W is transistor channel width, and L is transistorchannel length; and V_(T) is a threshold voltage of each MOSFET. It isnoted that in alternative embodiments of this invention, the output Voutof the circuit may be taken directly (instead of indirectly via C_(int))from the sensing capacitors C1-C4.

Transistors Q3 and Q4 are optional. In certain example embodiments,these transistors may be at low voltages (e.g., off) during the writephase, and on during the erase phase.

The output signal Vout (or Vo) of the FIG. 4 (and FIG. 5) sensingcircuit is sigma-delta modulated in certain example embodiments of thisinvention. Sigma-delta modulators, which can be used in a sigma-deltadigital-to-analog converter (DAC), can provide a degree of shaping orfiltering of quantization noise which may be present. Examplesigma-delta modulators that may be used are described in U.S. Pat. Nos.6,975,257, 6,972,704, 6,967,608, and 6,980,144, the disclosures of whichare hereby incorporated herein by reference. In certain examples ofsigma-delta conversion, oversampling, noise shaping and/or decimationfiltering may be brought to bear. Example advantages of sigma-deltamodulation include one or more of: (i) analog anti-aliasing filterrequirements are less complex and thus may be cheaper than certainexample nyquist based systems; (ii) sample and hold circuitry may beused due to the high input sampling rate and low precision A/Dconversion; (iii) since digital filtering stage(s) may reside behind theA/D conversion, noise injected during the conversion process such aspower-supply ripple, voltage reference noise and noise in the A/Dconverter itself may be controlled; (iv) since the sigma-delta convertermay be essentially linear it may not suffer from appreciabledifferential non-linearity and/or background noise level(s) may beindependent of input signal level. Improved S/N ratios may be realized.

FIG. 25 which is a simplified version of a sigma-delta modulator systemaccording to an example embodiment of this invention, for modulatingand/or converting the output signal Vout (or Vo) of the FIG. 4 (and FIG.5) circuit. In FIG. 25, a write pulse (see pulse at the bottom of FIG.25), is used to charge the sensing capacitor (C1, C2, C3 or C4) asexplained above with respect to FIG. 5. The square wave excitation(e.g., for writing and/or erasing cycles) is used on the sensingcapacitor to charge and discharge it. This process is mirrored ormimicked, for C_(int) as explained herein. The output signal Vout (orVo) of the FIG. 4 circuit is sigma-delta modulated by sigma-deltamodulator 60. The modulator 60 make take the form of a hardware circuit,firmware, and/or software in different example embodiments of thisinvention. Clock pulses 62 from a clock are input to the modulator 60,which trigger the latch of a quantizer of the modulator 60. After theoutput signal Vout (or Vo) are sigma-delta modulated by modulator 60,the modulated signals 64 are forwarded to an optional digital filter 66(e.g., lowpass filter or the like). Digital filter 66 processes thesigma-delta modulator digital output 64, which is a stream of 0s and 1s.The data is then scaled appropriately using calibration coefficient(s).The filtered data 68 is then read through a serial interface 69 or thelike and sent to a computer which does the correlation calculations forchunks of data packets. Thus, the data from the interface 69 is thencorrelated (e.g., autocorrelated and/or cross-correlated) as explainedherein. FIG. 26 is similar to FIG. 25, except that FIG. 26 illustratesan array of sensing capacitors C1-C4 which are multiplexed via amultiplexer.

FIG. 27 is a block diagram illustrating an example of sigma-deltamodulation which may be performed in the modulator 60 of FIGS. 25-26.Again, this modulation may be performed by circuitry, firmware and/orsoftware in different example embodiments of this invention. The analogoutput signal Vout (or Vo) of the FIG. 4 (and FIG. 5) circuit isreceived by a summer 70 of the sigma-delta modulator 60. Summer 70receives the analog Vout (or Vo) signal as well as a feedback signalfrom a feedback loop 71 of the modulator 60. The output of summer 70 isreceived by integrator 72 whose output is received by a quantizer 74such as a one bit quantizer. The digital output 64 is then filtered 66as explained above, and so forth. The sigma-delta modulation isadvantageous in that it provides oversampling and allows noise such asEMI to be treated and its adverse effects reduced. In particular, thenoise is spread by the sigma-delta modulation out over the frequencyband so that the signal-to-noise (S/N) ratio can be improved.

Referring back to FIG. 4, each capacitor (C1, C2, C3, C4) is dischargedbefore charging the next, in certain example embodiments of thisinvention. The process of discharging each capacitor is described inconnection with the erase pulse, with respect to FIGS. 5-6.

FIG. 5 is a circuit diagram of the FIG. 4 sensing circuit, with respectto an erase cycle. During an erase cycle, a previously charged capacitor(C1, C2, C3 or C4) is discharged before the next write cycle. FIG. 6includes example signals used during the erase cycle(s). No reading isperformed during the erase phase, in certain example instances. Duringan erase cycle or phase, Q7 is turned off (the write pulse Clk_(Wr) isnot present), and transistors Q5 and Q6 are turned on by an erase pulseClk_(Er) (see also FIG. 6). Thus, the capacitor (C1l, C2, C3 and/or C4)discharges to ground (e.g., V=0) or virtual ground (VG), as doesC_(int). Again, C_(int) mimics the capacitance of the sensor Cs. Oncethe capacitances Cs and C_(int) have been connected to ground anddischarged, the erase pulse and cycle ends. Then, the next capacitor(C1, C2, C3 or C4) in the sequence can be prepared, charged, and read.

Thus, referring to FIGS. 4-6, it will be appreciated that according tocertain example embodiments of this invention a rain sensor comprises: asensing circuit comprising at least first and second sensing capacitors(e.g., C1 and C2) that are sensitive to moisture on an external surfaceof a window, and at least one mimicking capacitor (C_(int)) that mimicsat least charging and/or discharging of at least one of the first andsecond sensing capacitors; wherein a writing pulse (Clk_(Wr)) causes atleast the first sensing capacitor (e.g., C1) to be charged, and anerasing pulse (Clk_(Er)) causes each of the first sensing capacitor(e.g., C1) and the mimicking capacitor (C_(int)) to substantiallydischarge; wherein presence of rain on the external surface of thewindow in a sensing field of the first sensing capacitor (e.g., C1)causes a voltage (see Vo or Vout) at an output electrode of themimicking capacitor (C_(int)) to fluctuate in a manner proportional tofluctuation of voltage at an output electrode (8) of the first sensingcapacitor (e.g., C1), even though the rain is not present in a field ofthe mimicking capacitor (C_(int)); and wherein rain is detected based onan output signal (see Vo or Vout) from the output electrode of themimicking capacitor (C_(int)), wherein the output signal is read atleast between an end of the writing pulse (Clk_(Wr)) and a beginning ofthe erase pulse (Clk_(Er)) (see the “read” area in FIG. 6).

Still referring to FIG. 5, in certain example embodiments of thisinvention, during the erase cycle, the erase pulse Clk_(Er) causes thecapacitor (C1, C2, C3 and/or C4) and thus also the mimicking capacitanceC_(int) to discharge to ground (e.g., a fixed potential such as V=0)(see the conventional ground symbol in FIG. 5). However, in otherexample embodiments of this invention, it has been found that a fixedground can lead to certain problems. Thus, in such other exampleembodiments of this invention, during the erase cycle the erase pulseClk_(Er) causes the capacitor (C1, C2, C3 and/or C4) and thus also themimicking capacitance C_(int) to discharge to a virtual ground VG thatis floating (see VG and the ground symbol in FIG. 5). Stated anotherway, an electrode of each of capacitors C1-C4 is floating. It may be ata floating or reference potential/voltage. It has been found that afloating or virtual ground can be highly advantageous in certain exampleembodiments of this invention (e.g., a floating ground and/or capacitorelectrode(s) can lead to a significant reduction in EMI interferenceproblems). For example, such a floating or virtual ground may helpreduce the chance of the sensor system being tricked by EMIinterference. In this respect, reference is made to FIGS. 28(a) and28(b) (along with FIG. 5).

In FIGS. 28(a)-(b), reference numerals 7 and 8 refer to the electrodesof a capacitor (e.g., C1, C2, C3 or C4). In these figures, “q” refers tocharge and Φ refers to potential (Φ1 is different than Φ2). In FIG.28(a) the capacitor (e.g., C1) is grounded at a fixed potential such as0 volts (the charge at grounded electrode 7 is fixed at +q). In thisrespect, when the charge at grounded electrode 7 is fixed at +q, whenone brings an external body E_(B) (e.g., human finger with a higherdielectric constant) into a sensing area of the capacitor (e.g.,touching the front surface of the windshield over the capacitor) thisexternal body induces a change in charge −Δq and the other electrode 8which is not fixed changes from a charge of −q to a charge of −q +Δq inan attempt to balance charge. Thus, if one were to ground the capacitorat a fixed potential such as 0 volts, and read an output voltage of thecapacitor, one would read charge changes caused by Δq which is notneeded, and this may lead to false readings. Comparing FIGS. 28(a) and28(b), FIG. 28(b) illustrates an advantage of causing an electrode 7 ofthe sensing capacitor (e.g., any of C1-C4) to be floating (e.g., at afloating or virtual ground). In FIG. 28(b), the charge q at electrode 7is not fixed. E.g., the charge at electrode 7 changes from +q′ to +q″when the external body comes into contact with the windshield at asensing area of the capacitor, thereby indicating the floating nature ofthe electrode. In FIG. 28(b), when the external body (e.g., humanfinger) is applied to the windshield over the capacitor sensing area thefree charges on both electrodes 7 and 8 of the capacitor change. Thus,the adverse effect of Δq is eliminated or reduced by using the floatingor virtual ground VG (electrode 7 is floating). In particular, whenelectrode 7 is floating as in FIG. 28(b), the external body (E_(B)) doesnot adversely affect summation of charge because adding the charges (+q″and −q″) of the electrodes 7 and 8 when the external body is presentgives zero or substantially zero. False readings due to EMI interferencecan also be reduced by using this floating feature. Thus, in certainexample embodiments, the floating nature may allow the absolute valuesof the charges q at capacitor electrodes 7 and 8 to be the same orsubstantially the same even when the external body is present since theelectrode 7 is floating and is not fixed at ground. This is one examplereason why it may be advantageous to cause the electrodes 7 of thecapacitors C1-C4 to be floating, or be at a virtual ground VG as shownin FIG. 5. Thus, referring to FIGS. 5 and 28, the sensing capacitorsC1-C4 are floating and both electrodes thereof are isolated from ground.Accordingly, according to certain example embodiments of this invention,the rain sensor comprises at least one sensing capacitor (C1, C2, C3and/or C4) that is sensitive to moisture on an external surface of awindow, the sensing capacitor including a first capacitor electrode (8)that receives a charging signal and a second capacitor electrode (7)spaced apart from the first capacitor electrode (8); and wherein thesecond capacitor electrode (7) is floating so that the sensing capacitoris isolated from ground.

FIG. 6 is an exemplary timing diagram of signals applied to or read outfrom the FIG. 4-5 circuit during the write and erase modes/cycles. Asnoted above, the capacitors (C1-C4) are sequentially charged, read,quantized, and erased. FIG. 6 shows a clock write (Clk_(Wr)) and erase(Clk_(Er)) pulse for each capacitor C1-C4, in sequence. Then, voltagesare quantized and output. Variable output voltage Vo1-Vo4 correspond tocapacitors C1-C4 respectively, and thus C_(int). It is noted that theoutput signals Vo1-Vo4 in FIG. 6 are taken at V_(out) (or Vo) in FIGS.4-5. Moreover, in FIG. 6, the output signals Vo are read or analyzed(e.g., for autocorrelation and/or cross-correlation) at the peak readareas (see “Read” in FIG. 6) of the output signals where the outputsignals are substantially stabilized and/or the capacitor saturated. Inparticular, the output signal V_(out) (or Vo) in FIG. 6 for a particularcapacitor (C1) is read in the “read area” after the end of the writepulse (Clk_(Wr)) for that capacitor, and before and/or up to thebeginning of the erase pulse (Clk_(Er)) for that capacitor.

Still referring to FIG. 6, for example, a drop of water on the exteriorsurface of a windshield will affect the magnitude of the outputsignal(s) V_(out) (or Vo). For instance, a water drop over the area of agiven capacitor (e.g., C1) will cause the level of the output signal(s)V_(out) (or Vo) for that capacitor in the “read” area of the signal tobe higher compared to a situation where no such drop was present. Theexact magnitude or level depends on the size of the water drop. Withincreasing water amounts, the magnitude of the signal at the “read” areagets higher because the dielectric constant of water is higher than thatof glass and/or air and this causes the capacitance to increase. In asimilar manner, if no water drop is present on the windshield over thearea of a given capacitor (e.g., C1) then this will cause the level ofthe output signal(s) V_(out) (or Vo) for that capacitor in the “read”area of the output signal to be lower compared to a situation where adrop was present.

The signals (e.g., from the capacitor(s)) may be converted fromanalog-to-digital via a sigma-delta modulation scheme or the like, whichmay be implemented at the software level or in any other suitable mannersuch as via hardware. The principle behind sigma-delta architecture isto make rough evaluations of the signal, to measure the error, integrateit, and then compensate for that error. Data may be oversampled at agiven rate of at least 32 kHz, e.g., more preferably 64 kHz, though itwill be appreciated that other sampling rates may be used. The coursequantization can be recovered by the sigma-delta modulation scheme toproduce a simple binary 0 or 1 output, corresponding to on and off,respectively. Thus, the sigma-delta modulation scheme may be used toreduce noise (e.g., at the tail of the signal) and produce a digitaloutput stream (e.g., 1s and 0s).

Before discussing the detailed operation of and example mathematicsbehind an example sensor algorithm, an overview of the states in whichthe sensor and/or wipers can take will be given in connection with FIG.7, which is an exemplary state diagram showing how autocorrelation andcross-correlation data may be used to control vehicle wipers. The systembegins in Start/Initialization State S702. In this state, all buffersare cleared in certain example instances. Based on the inputs ofcapacitors C₁, C₂, . . . , C_(n), analog-to-digital conversion of thesignals from the respective inputs is accomplished via sigma-deltamodulation. Data is read for the plurality of channels over time periodT. Operating Mode Selector State S704 functions as a switch to selectbetween the manual or automatic wiper mode. If Operating Mode SelectorState S704 indicates that manual mode is selected, then in Manual ModeState S706 an auto mode may be disabled and a pre-existing manual modeenabled. Then, the system returns to Start/Initialization State S702.However, if Operating Mode Selector State S704 indicates that auto modeis selected, the automatic wiper mode is enabled in Auto Mode StateS708.

In Autocorrelator Engine State S710, at least three computations areperformed. First, a normalized autocorrelation is calculated for eachsignal input of the capacitive array. Second, the gradient of theautocorrelation is calculated. Third, the difference between the signalinput and a reference non-disturbed signal (Δ₁) may be calculated. Thisinformation is passed to Is Raining? State S712, in which at least threeconditions are checked to determine whether it is likely that it israining, there is moisture on the windshield, etc. Likely indications ofrain are that the gradient of the autocorrelation is greater than 1, allautocorrelation values are positive, and/or Δ₁ is greater than somepre-defined threshold value t1. If these conditions are not met, thesystem moves to Park Wipers/Stop Motor State S714, where wipers areparked (if they are moving) or not actuated, and the motor is stopped(if it is engaged), and the system is returned to Start/InitializationState S702.

On the other hand, if all conditions are met (e.g., it is likely thatthere is an interaction of water, moisture or some other perturbation onthe glass, etc.), the system moves to Lowest Speed State S716, in whichthe wiper motor is activated at the lowest speed available. InCross-Correlator Engine State S718, the cross-correlation between theinput signals from the capacitors is calculated. The cross-correlationcurve shape is determined, and the symmetry of the two sides of thecross-correlation curve are checked for symmetry. As will be describedbelow, these checks help, for example, to determine the type ofperturbation (e.g., light rain, heavy rain, fog, snow, etc.) hitting thewindow (e.g., windshield). In Rain Degree Assessment State S720, the“degree of rain” (e.g., heavy, light, etc.) is determined. Based on thisdetermination, the wiper motor is activated at the appropriate speed inSpeed Selector State S722. Lastly, the system is returned toStart/Initialization State S702 to determine whether there is any changein conditions outside the car.

The steps performed by the rain sensor will be described in greaterdetail in connection with FIG. 8, which is an exemplary flowchartshowing how autocorrelation and cross-correlation data can be used tocontrol wipers in certain example embodiments of this invention. In FIG.8, in step S800 buffers are cleared, and data outputted from the FIG.4-5 circuit (e.g., from C_(int), or from capacitors C1-C4) issigma-delta modulated, and is read in S802.

The algorithm for determining whether to engage wipers and, if so, thespeed at which to engage wipers begins by autocorrelating thesigma-delta modulated data in step S804. Autocorrelation may be used foranalyzing functions or series of values, such as time domain signals. Anautocorrelation is the cross-correlation of a signal with itself.Autocorrelation is used for finding repeating or substantially repeatingpatterns in a signal, such as, for example, determining the presence ofa periodic signal buried under noise, identifying the fundamentalfrequency of a signal that does not actually contain that frequencycomponent but implies within it with many harmonic frequencies, etc.Cross-correlation is a measure of the similarity of two signals, and itis used to find features in an unknown signal by comparing it to a knownone; in other words it may be used to perform signal fingerprinting incertain instances. Cross-correlation is a function of the relative timebetween the signals. In certain example embodiments of this invention,digital signals from any two capacitors (e.g., C1 and C2) arecross-correlated, in close spatial proximity, and the system looks forany degree of correlation at time lags other than a time lag of zero.This spatio-temporal cross-correlation allows the system to extractpatterns in how the falling rain is electrically projecting itself overthe sensor array. As an example, the system may take the case of raindrops moving over one capacitor C1 at a time t0 and the same drop“ringing” another capacitor C4 (spatially separated by distance L fromC1). If the drop moves at an average speed Vi, the time (t0+T), whereT=L/Vi, the cross-correlation function will have another extremum orkink. The normalized magnitude of this extremum value may allow thesystem to determine the degree of rain falling on the sensor.

Each capacitor C1-C4 has an autocorrelation function associated with thedigitized Vout resulting from the readout thereof (or the correspondingreadout of C_(int)). In example embodiments, the autocorrelationfunction depends on time difference, rather than on actual time.Computing autocorrelations is beneficial because it allows, for example,the deduction of the fundamental frequency irrespective of phase.Autocorrelations are advantageous over other methods, such as Fouriertransforms (which may also be used in certain example embodiments ofthis invention) which provide information about the underlying harmonicsonly. Thus, the use of autocorrelation of the readouts from capacitorsC1-C4 (which as explained above, includes the corresponding readoutsfrom mimicking C_(int)) can be used to detect and distinguish betweenbeads of water, dirt, dust, droplets, downpour, etc.

It is noted that herein data from C_(int) is considered to be data fromthe capacitors C1-C4 because the capacitance C_(int) mimics orsubstantially mimics the capacitances C1-C4 as explained above. Thus,when we talk about receiving data from the capacitors (e.g., C1-C4),this covers and includes receiving data from capacitance C_(1nt). Inother words, the output from the FIG. 4-5 circuit is considered to befrom the capacitors C1-C4, even though it is not taken directlytherefrom.

Rain, as a function of time, may be represented by the followingformula: ${b\left( {\overset{\_}{r},t} \right)} = \left\{ \begin{matrix}1 & {{rain}\quad{projects}\quad{electrically}} \\0 & {otherwise}\end{matrix} \right.$Essentially, b takes on a binary value indicating whether it is raining(1), or not (0). It will be appreciated that b is at least two bits, andthat for sigma-delta modulation 24-bits may be used in certain exampleembodiments. It also will be appreciated that a scale could beintroduced, potentially to capture more data related to the voltages inthe capacitors C1-C4 (or C_(int)).

At the end of a sampling cycle L, for example, the output from the FIG.4-5 circuit, e.g., from the array of four capacitors C1-C4 (or viaC_(int)), ranges from 0000 to 1111 in certain example embodiments, usingbinary digital data. A single bit turned on can initiate a single wipein certain example instances. In the case when all bits are off (0000)or all bits are on (1111), then no wipes may be initiated in certainexample instances, because likely there is nothing on the windshield,the car is completely submerged, etc., since all capacitors in the arraywould be reading the same which is not consistent with rain falling on awindow. Thus, the most probable events where wipers will be needed arethose in the range of 0001 to 1110 (i.e., when the output from allcapacitors in the array is not the same). When the data falls in thisrange, or even if it does not fall within this range, correlationfunctions (auto and/or cross correlation functions) may be performedusing the following integral. It will be appreciated that the integralbelow can be rewritten in other forms, such as, for example, as asummation. The correlations between two drops over a large time periodmay be computed according to the following formula:${R_{b}\left( {r_{1},{t;r_{2}},t_{2}} \right)} = {\frac{1}{L}{\int_{0}^{L}{{b\left( {r_{1},{t_{1} + t}} \right)}\quad{b\left( {r_{2},{t_{2} + t}} \right)}{\mathbb{d}t}}}}$${R_{b}\left( {r_{1},{t;r_{2}},t_{2}} \right)} = {R_{b}\left( {{\Delta\quad\overset{\_}{r}},{\Delta\quad t}} \right)}$where R_(b) is the correlation of a binary event, given as a function ofthe resistances r_(i) at given times t_(i).; and L is a large samplingperiod during which a burst of data is captured. In certain exampleembodiments, the sampling period L may be from about 10 to 100 ms, andmore preferably from about 20-30 ms, which corresponds approximately tothe frequency an average human eye can discern. R_(b) also is equal to afunction of the correlation of the changes in resistances acrosscapacitors Δ{right arrow over (r)} and the change in time. When Δ{rightarrow over (r)}=0, the autocorrelation value is determined since datafrom the same capacitor is being analyzed, and when Δ{right arrow over(r)}≠0, cross-correlations are computed since correlation is performedon data from different capacitors.

These functions are subject to several example constraints andunderlying assumptions. First,Δ{right arrow over (r)}=V{right arrow over (i)}Δt.This constraint essentially means that a drop of water or the like ismoving at a given time scale. Second,b({right arrow over (r)}+V{right arrow over (i)}Δt,t+Δt)=b({right arrowover (r)},t).This constraint mimics or substantially mimics what happens when dropsof water or the like move from one capacitor to another. Thus, thecorrelation functions might be thought of as discrete steps p in spaceand T in time. This feature may be mathematically represented as thefollowing equation:R _(b)(m{right arrow over (p)},nT)≡R(V{right arrow over (i)}Δt,Δt)Essentially, the left-hand side of the equation establishes atheoretical grid in space and time across which a drop of water or thelike moves. For example, FIG. 9 is an exemplary stylized view of how arain droplet might travel across a windshield. FIG. 9 shows a raindroplet moving across a windshield on the X-Z plane during an initialtime period (t=0) and some late quantum of time (t=T). The assumptionthat drop distribution is uniform over space and time allows thecreation of a binary field caused by rain that is in a wide sensestationary. The system also assumes that the temporal correlationbetween preferred pixels in the same neighborhood is high in thedirection of rain. Lastly, the degree of autocorrelation andcross-correlation in time quantifies rain fall and other disturbances.

It will be appreciated that in certain example embodiments,computational time can be saved because of the nature of correlationmatrices and the nature of rainfall. For example, correlation matricesmay be symmetrical in certain example instances. Additionally, asanother example, because rain tends to fall down from the sky and moveup along a windshield, it may be sufficient to compare only capacitorsthat are disposed vertically relative to one another incross-correlation, while ignoring horizontally adjacent capacitors.

It is noted that while binary data is used in certain exampleembodiments of this invention, this invention may also utilized greyscale data in certain example instances with respect to outputs from thecircuit of FIGS. 4-5, or from similar or other suitable circuit(s).

After the autocorrelation has been performed in step S804 (e.g., usingthe equation(s) discussed above, or some other suitable correlationequation(s)), one or more checks may be performed to enhance theaccuracy of the system. Examples of such checks (e.g., if theautocorrelated data Rxx has negative values, if a gradient is greaterthan one, and/or if the shape of a Rxx curve is different orsubstantially different from a normalized non-disturbed autocorrelationdata stored in memory) are listed in the bottom part of the box for stepS804 in FIG. 8. One, two or all three of these checks may be performed.

For example, one check of the autocorrelation data in step S806 may beto determine whether the autocorrelated data from one or more of thecapacitor(s) (C1, C2, C3 and/or C4; or via mimicking C_(int)) comprisesnegative values. For instance, when the autocorrelated data has negativevalue(s), then the system or method may indicate that it is not raining,may park the wipers, and/or may not actuate windshield wipers (see stepS808). This check is for determining, for example, whether a detecteddisturbance is actually rain. In this respect, FIG. 10 is a graphplotting example experimentally-obtained maximum values ofnon-normalized autocorrelations for different disturbances. FIG. 10illustrates that water signals are greater than non-disturbed signalsand are positive, and that external interferences such aselectromagnetic waves from CB radios and human hand touching of a windowtend to be below the no-disturbance levels and may be negative. Thus, toeliminate or reduce false detections due to external disturbances suchas, for example, a human hand touching the window, radio signalinterference, etc., any signal with negative autocorrelation values isconsidered a “no-rain” event. It will be appreciated that some exampleembodiments may consider negative autocorrelation values. Other exampleembodiments may take other measures to eliminate or reduce falsedetections due to external interferences by, for example, comparinggradients (e.g., any curve lower or less than the no-disturbancecurve/plot of FIG. 10 may be considered a “no-rain” event), shieldingcapacitors, etc.

A second example check of the autocorrelation data is to check whether agradient of an autocorrelation curve associated with the autocorrelateddata is greater than one; and if not then the system or method mayindicate that it is not raining, park the wipers and/or not actuatewipers of the vehicle (see step S808). In this check, the gradient ofthe normalized autocorrelation of the disturbance is checked. Thegradient of the normalized autocorrelation of a non-disturbed signal isclose to 1. Measuring the gradient is beneficial because it is notaffected by temperature change. Thus, the rain sensor may besubstantially immune to false reads due to temperature changes incertain example embodiments of this invention. In certain exampleinstances, gradients less than 1 (or some other predetermined value) maybe considered no-rain events.

A third example check of the autocorrelation data is to determinewhether there is a match or substantial match between an autocorrelationcurve associated with the autocorrelated data and one or morepredetermined autocorrelation curve(s) stored in a database and/ormemory. When the shape of the autocorrelation curve associated with theautocorrelated data from the FIG. 4-5 circuit is different orsubstantially different from an autocorrelation curve relating tonormalized non-disturbed autocorrelation data, this may be considered ano-rain event and it may be indicated that it is not raining, wipers maybe parked, and/or wipers may be not actuated (see step S808). However,when there is a match or substantial match between the autocorrelationcurve associated with the autocorrelated data from the FIG. 4-5 circuitand a predetermined autocorrelation curve associated with moisture suchas rain, then it may be indicated that it is raining, wipers mayactuated, or kept moving.

In this regard, the shape of the autocorrelation curve may be used toreduce false wipes and/or false detections. In particular, thenormalized autocorrelation of a non-disturbed signal is used as areference. Then, the normalized autocorrelation of each signal capturedfrom the FIG. 4-5 circuit is compared to the reference to identify theclosest fingerprint in certain example instances. Generally, the morewater present in the sensing area, the larger the difference between thereference signal and the observed signal. In this way, correlationsnapshots can be compared to reference snapshots of well-known eventssuch as the presence of rain, dirt, no-disturbance, ice, and so forth.In general, correlation snapshots may be normalized, though theinvention is not so limited. Correlation snapshots preferably plotr-values versus quantums of time over a discrete time interval incertain example embodiments of this invention.

In certain example embodiments, when there is a match or substantialmatch between the autocorrelation curve associated with theautocorrelated data from the FIG. 4-5 circuit and a predeterminedautocorrelation curve associated with a non-moisture substance such asdirt, then this may be considered a no-rain event and it may beindicated that it is not raining, wipers may parked and/or not actuated(see step S808).

Thus, it will be appreciated that the shape of the autocorrelation curveresulting from the data output from the FIG. 4-5 circuit (from thecapacitors C1-C4, or via C_(int)) may be used to reduce false wipes as athird condition. For instance, a normalized autocorrelation curve of anon-disturbed signal may be used as a reference. Then, the normalizedautocorrelation of each signal captured from the FIG. 4-5 circuit iscompared to the reference to identify the closest fingerprint.Generally, the more water present in the sensing area, the larger thedifference between the reference signal and the observed/detectedsignal. In this way, correlation snapshots can be compared to referencesnapshots of well-known events. In general, correlation snapshotspreferably are normalized, though the invention is not so limited.Correlation snapshots preferably plot r-values versus quantums of timeover a discrete time interval.

A potential problem with capacitive rain sensors is that rapidtemperature changes (e.g., due to the radiation absorbing black fritused to cosmetically hide the sensor pattern) change the dielectric“constant” (permittivity) of the glass. This is then registered as acapacitance change and may erroneously be interpreted as a rain signal.However, according to certain example embodiments of this invention, anormalized autocorrelation function is unchanged, or substantiallyunchanged, for different temperatures even though there may bedifferences for the non-normalized autocorrelation functions for thedifferent temperatures. Thus, in certain example embodiments of thisinvention, the sensing system is unaffected or substantially unaffectedby temperature changes.

In addition, extremely slow accumulation of water like ultra-fine mistcan slowly build up to a level that triggers sensors based on Nyquistrate converters. In the time of observation that concerns human vision(e.g., 30-60 Hz), the autocorrelation function in certain exampleembodiments of this invention is able to discriminate between theultra-slow accumulation of dew or condensation and normal mist and rain.

FIGS. 11A-11D provide sample experimentally-obtained correlationsnapshots. These correlation snapshots, or fingerprints of an event, canbe stored as reference fingerprints or correlation curves.Observed/detected correlation snapshots (e.g., autocorrelation curves)can be compared to these reference fingerprints to determine the type ofevent occurring. For instance, FIG. 11A is an experimentally-obtainedautocorrelation snapshot indicative of heavy rain. FIG. 11B is anexperimentally-obtained autocorrelation snapshot indicative of a lightmist. FIG. 11C is an experimentally-obtained autocorrelation snapshotindicative of CB radio interference. FIG. 11D is anexperimentally-obtained autocorrelation snapshot indicative of agrounded body with a voltage. It will be appreciated that thesefingerprints are provided as non-limiting examples and reflectexperimentally-obtained data. Actual events may differ in variouscharacteristics. Thus, in certain example embodiments of this invention,when it is determined that there is a match or substantial match betweenthe autocorrelation curve associated with the autocorrelated data fromthe FIG. 4-5 circuit and a predetermined non-moisture autocorrelationcurve such as that of FIG. 11C or FIG. 11D, then this may be considereda no-rain event and it may be indicated that it is not raining, wipersmay parked and/or not actuated (see step S808). However, in certainexample embodiments of this invention, when it is determined that thereis a match or substantial match between the autocorrelation curveassociated with the autocorrelated data from the FIG. 4-5 circuit and apredetermined moisture-related autocorrelation curve such as that ofFIG. 11A or FIG. 11B, then this may be considered a rain event and itmay be indicated that it is raining, wipers may actuated and/or keptmoving. In addition to the predetermined autocorrelation curves of FIGS.11A-11D, other reference fingerprints may be stored and/or compared withobserved correlation snapshots in other example embodiments of thisinvention.

Turning back to FIG. 8, in step S806 it is determined whether each ofthe three conditions set forth in the bottom portion of the S804 box ismet. In particular, it is determined in S806 whether each of thefollowing is met: (a) the autocorrelated data has no negative values;(b) a gradient of an autocorrelation curve associated with saidautocorrelated data is greater than a predetermined value such as one;and (c) the shape of the autocorrelation curve associated with theautocorrelated data from the FIG. 4-5 circuit is different than apredetermined autocorrelation curve associated with non-disturbedautocorrelation data. If they are not all met, this is an indication ofa non-rain event and the process moves to step S808 where the vehiclewiper(s) are parked (if they were moving) or are kept off, and beginsinitialization S800 again. However, if all of these requirements are metin S806, then the process moves to S810 and the vehicle's wipers (e.g.,windshield wipers) are activated at their lowest speed.

For purposes of example only, and understanding, FIG. 13 illustrates anexample of autocorrelation. In FIG. 13, the values from (or relating to)sensing capacitor C1 are, at sequential times −t2, −t1, t0, t1, t2 andt3 are 0, 0, 1, 1, 0 and 0, respectively. Autocorrelation for time 0(aco) is determined by multiplying the values relating to C1 in anon-offset manner, and then adding or summing the results. It can beseen in FIG. 13 that aco is equal to 2 in this instance. Thus, on theautocorrelation graph at the bottom of FIG. 13, an entry in the graph attime 0 is made for an autocorrelation value of 2. Note that theautocorrelation graph at the bottom of FIG. 13 is similar, but simpler,that the autocorrelation graph in FIG. 10 and the autocorrelation valuesmay be obtained for FIG. 10 in a like manner. Next, still referring toFIG. 13, autocorrelation is performed using the capacitance valuesrelating to Cl for the next point in time to obtain autocorrelationvalue ac1. This next autocorrelation value (ac1) is obtained by shiftingthe bottom row sequence of values for C1 relative to the top row asshown in FIG. 13, and then multiplying the values in the rows which lineup with each other and summing the results. FIG. 13 illustrates that ac1is equal to 1 for time 1. Thus, this autocorrelation value of 1 for timet1 may be entered in the graph at the bottom of FIG. 13 and a line isdrawn between the two entered data points for purposes of example andunderstanding. The, for the next time value (or lag), the bottom row isagain shifted another segment over relative to the top row and theprocess repeated, and so forth. It can be seen that the autocorrelationplots in FIG. 10 may be obtained in a similar manner. In FIG. 13, itwill be appreciated that cross-correlation may be performed by replacingthe C1-related values in the bottom row with values from or related toanother capacitor such as C2 (or C3 or C4).

Examining autocorrelation and/or cross-correlation also can helpdistinguish between, for example, light rain and heavy rain. Forexample, if only the autocorrelation in time is high (andcrosscorrelation is low), then there probably is only light rain. FIG.12A is an exemplary correlation matrix showing light rain. Of note inFIG. 12A is that the correlations between C1 and C1, C2 and C2, C3 andC3, and C4 and C4 (these are autocorrelations) over a given time periodare high, while the rest of the correlations (the cross-correlations)are low. By hypothesis and confirmed experimental data, a matrix of thissort would indicate a light rain.

On the other hand, if both autocorrelation and cross-correlation in timebetween capacitor signals are high, there is probably fast rain. FIG.12B is an exemplary correlation matrix showing heavy rain. In FIG. 12B,not only are the autocorrelations of individual capacitors high (i.e.,the autocorrelations are the correlations between C1 and C1, C2 and C2,C3 and C3, and C4 and C4), cross-correlations between differentcapacitors also are generally high (the correlations in FIG. 12B goingdiagonally from the upper-left to the bottom-right are theautocorrelations, and the rest are the cross-correlations). Byhypothesis and confirmed experimental data, a matrix of this sort wouldindicate a fast rain. The degree of cross-correlation can be quantizedto determine the relative speed of the rain. This data can, in turn, beused to trigger various wiper speeds, as appropriate for the speed ofthe rain. For instance, the more cross correlations that are high, thehigher the wiper speed to be used.

More systematically, in step S812, cross-correlations are computed(correlations between data relating to different capacitors), and thetwo sides of the cross-correlation curve are used to determine asymmetry level L. If the symmetry level is lower than a predefinedthreshold t_(min), step S814 directs the system to step S816 wherewipers are activated at the lowest speed, and the system is returned toinitialization step S800. If the symmetry level is greater than t_(min)but less than an arbitrary value t, step S818 directs the system to stepS820 where wipers are activated at a faster or medium speed, and thesystem is returned to initialization step S800. It will be appreciatedthat a plurality of arbitrary values t_(i) may be specified, and asymmetry level falling between t_(i) and t_(i+1) will activate anappropriate corresponding wiper speed and then return the system toinitialization step S800. Finally, in step S822, if the symmetry levelis above a predefined level t_(max), step S822 directs the system tostep S824 where wipers are activated at the highest speed, and thesystem is returned to initialization step S800. Thus, correlations fromthe data output from the FIG. 4-5 circuit can be used to adjust wiperspeed. In certain example embodiments, the more cross correlations thatare high, the higher the wiper speed to be used due to the likelihood ofheavier rain.

For purposes of example and understanding, FIGS. 14-24 illustrateexamples of cross-correlation performed according to certain exampleembodiments of this invention. FIG. 14 sets forth cross-correlation datain certain example instances, whereas FIGS. 15-24 illustratecross-correlation graphs of certain of the data from FIG. 14 where rainis detected. In FIGS. 15-24, each lag on the horizontal axis is onemicrosecond (1 μs) for purposes of example, and sampling was performedevery one microsecond. As explained above with respect to FIG. 13, inFIGS. 15-24 at time=0 (lag 0), there is no shift in time of the valuesfrom the different capacitors being correlated. FIG. 14 illustrates thatwhen rain was present (see signals S1-S5 and W1-W5), the delta signalsrelating to autocorrelation were high. FIGS. 15-24 are cross-correlationplots relating to these signals. It is helpful to look for symmetrybetween the plots on the left and right hand sides of each of FIGS.15-24 (one side of zero is compared to the other side of zero).Generally speaking, if there is symmetry about the zero lag axis, thereis not much cross-correlation which indicates that the detected rain isnot very hard. However, if there is asymmetry about the zero lag axis,then this means more cross-correlation and indicates that the rain ishard or harder. For example, note the asymmetry in FIGS. 18, 19 and 23about the zero lag axis due to the bumps or valleys on one or bothsides. More cross-correlation indicates that the rain drops are movingfrom one capacitor's sensing area to another capacitor's sensing area.In this respect, each interaction of a rain drop and the surface of awindshield has its own correlation signature in the time domain. Highcross-correlation indicates that the same drop is being detected atdifferent capacitors, at different points in time (e.g., see FIG. 9also). It is noted that the lower case “t” in FIG. 9 is the same as thelags axis in FIGS. 15-24.

Thus, it will be appreciated that certain example embodiments of thisinvention provide a moisture sensor (e.g., rain sensor) that can detectrain or other material on a vehicle window or other type of window orsheet/surface, without the need for a reference capacitor. Spatialtemporal correlation may be used. All capacitors, or a plurality ofcapacitors, in the sensing array may be identical or substantiallyidentical in shape in certain example embodiments. For purposes ofexample, at a given point in time (e.g., t1), the system may compareC1-relates values with C2 related values, and/or other capacitor relatedvalues. For this time t1, the system may also compare C1-related valueswith itself (autocorrelation), and may also compare autocorrelation forC1 with autocorrelation for C2 and/or other sensing capacitors).

It is noted that while capacitors C1-Cn (where n is two, four, ten orany other suitable number) are preferred as the sensing devices incertain example embodiments of this invention, it is possible to useother types of sensing devices instead of or in addition to thecapacitors in certain example instances.

While the invention has been described in connection with what ispresently considered to be the most practical and preferred embodiment,it is to be understood that the invention is not to be limited to thedisclosed embodiment, but on the contrary, is intended to cover variousmodifications and equivalent arrangements included within the spirit andscope of the appended claims.

1. A rain sensor comprising: a sensing circuit comprising at least firstand second sensing capacitors that are sensitive to moisture on anexternal surface of a window; the sensing circuit further comprising atleast one mimicking capacitor that mimics at least charging and/ordischarging of at least one of the first and second sensing capacitors;wherein a writing pulse causes at least the first sensing capacitor tobe charged, and an erasing pulse causes each of the first sensingcapacitor and the mimicking capacitor to substantially discharge;wherein presence of rain on the external surface of the window in asensing field of the first sensing capacitor causes a voltage at anoutput electrode of the mimicking capacitor to fluctuate in a mannerproportional to fluctuation of voltage at an output electrode of thefirst sensing capacitor, even though the rain is not present in a fieldof the mimicking capacitor; and wherein rain is detected based on anoutput signal from the output electrode of the mimicking capacitor,wherein the output signal is read at least between an end of the writingpulse and a beginning of the erase pulse.
 2. The rain sensor of claim 1,wherein the output signal from the output electrode of the mimickingcapacitor is converted from analog to digital, and thereafter issubjected to processing for determining whether rain is present on theexternal surface of the window.
 3. The rain sensor of claim 2, whereinsaid processing comprises autocorrelation and/or cross-correlation. 4.The rain sensor of claim 1, wherein the sensing circuit comprises anarray of sensing capacitors, and wherein the mimicking capacitor mimicseach of the sensing capacitors in the array which are sequentiallycharged and discharged.
 5. The rain sensor of claim 1, whereinelectrodes of the first and second sensing capacitors are floating sothat the sensing capacitors are electrically isolated from ground. 6.The rain sensor of claim 1, wherein the first and second sensingcapacitors have fractal geometry.
 7. The rain sensor of claim 1, whereinthe first and second sensing capacitors have the same, or substantiallythe same, geometry and size.
 8. The rain sensor of claim 1, wherein themimicking capacitor is physically separated from the sensing capacitors,and wherein the writing pulse causes the first sensing capacitor, butnot the second sensing capacitor, to charge and also causes themimicking capacitor to charge.
 9. The rain sensor of claim 1, furthercomprising: means for correlating data from the mimicking capacitor thatis related to at least one of the sensing capacitors to obtaincorrelated data; means for determining, based at least on the correlateddata, whether rain is present on an exterior surface of the window; andwherein said correlating is autocorrelating and/or cross-correlating.10. The rain sensor of claim 1, wherein each of the first and secondsensing capacitors comprise first and second spaced apart electrodesthat are substantially coplanar with one another.
 11. The rain sensor ofclaim 1, wherein the writing pulse is supplied to a gate of a firsttransistor that is electrically connected to the first sensingcapacitor, and the erasing pulse is applied to respective gates ofsecond and third transistors that are electrically connected to thefirst sensing capacitor and the mimicking capacitor respectively.
 12. Amethod of detecting rain on a surface of a window, the methodcomprising: supplying first and second spaced apart writing pulses whichrespectively cause first and second sensing capacitors of a sensingcircuit to charge, wherein the first sensing capacitor charges when thesecond sensing capacitor is substantially discharged, and the secondsensing capacitor charges when the first sensing capacitor issubstantially discharged, so that the first and second sensingcapacitors are charged at different times; each of the first and secondsensing capacitors being sensitive to moisture on the surface of thewindow; supplying a first erasing pulse, between times of the first andsecond writing pulses, the first erasing pulse causing the first sensingcapacitor to substantially discharge, and supplying a second erasingpulse after the second writing pulse wherein the second erasing pulsecauses the second sensing capacitor to substantially discharge; whereina magnitude of an output of the sensing circuit is affected by presenceof rain on the surface of the window; and converting an analog outputsignal of the sensing circuit to a digital signal and based on thedigital signal determining whether rain is present on the surface of thewindow.
 13. The method of claim 12, wherein each of the first and secondsensing capacitors comprise first and second spaced apart electrodesthat are substantially coplanar with one another and which are supportedby the window.
 14. The method of claim 12, wherein the writing pulsesare supplied to a gate of a first transistor that is electricallyconnected to at least one of the sensing capacitors, and the erasingpulses are applied to respective gates of second and third transistors.15. The method of claim 12, wherein the sensing circuit furthercomprises at least one mimicking capacitor that mimics at least chargingand/or discharging of at least one of the first and second sensingcapacitors, and the presence of rain on the surface of the window in asensing field of the first sensing capacitor causes a voltage at anoutput electrode of the mimicking capacitor to fluctuate in a mannerproportional to fluctuation of voltage at an output electrode of thefirst sensing capacitor, even though the rain is not present in a fieldof the mimicking capacitor; and detecting rain based on an output signalfrom the output electrode of the mimicking capacitor.
 16. The method ofclaim 12, further comprising: correlating data of the digital signal toobtain correlated data; determining, based at least on the correlateddata, whether rain is present on the surface of the window; and whereinsaid correlating is autocorrelating and/or cross-correlating.
 17. A rainsensor comprising: at least one sensing capacitor that is sensitive tomoisture on an external surface of a window, the sensing capacitorincluding a first capacitor electrode that receives a charging signaland a second capacitor electrode spaced apart from the first capacitorelectrode; and wherein the second capacitor electrode is floating sothat the sensing capacitor is isolated from ground.
 18. The rain sensorof claim 17, wherein the first and second capacitor electrodes of thesensing capacitor are substantially coplanar.
 19. The rain sensor ofclaim 17, wherein the rain sensor comprises first, second, third andfourth sensing capacitors that are sensitive to moisture on the externalsurface of the window, each of the four sensing capacitors including afirst capacitor electrode that receives a charging signal and a secondcapacitor electrode spaced apart from the first capacitor electrode; andwherein the second capacitor electrode of each of the four sensingcapacitors is floating so that each of the four sensing capacitors isisolated from ground.